# Copyright (c) OpenMMLab. All rights reserved.
import argparse
from collections import OrderedDict

import torch


def convert_stem(model_key, model_weight, state_dict, converted_names):
    new_key = model_key.replace("stem.conv", "conv1")
    new_key = new_key.replace("stem.bn", "bn1")
    state_dict[new_key] = model_weight
    converted_names.add(model_key)
    print(f"Convert {model_key} to {new_key}")


def convert_head(model_key, model_weight, state_dict, converted_names):
    new_key = model_key.replace("head.fc", "fc")
    state_dict[new_key] = model_weight
    converted_names.add(model_key)
    print(f"Convert {model_key} to {new_key}")


def convert_reslayer(model_key, model_weight, state_dict, converted_names):
    split_keys = model_key.split(".")
    layer, block, module = split_keys[:3]
    block_id = int(block[1:])
    layer_name = f"layer{int(layer[1:])}"
    block_name = f"{block_id - 1}"

    if block_id == 1 and module == "bn":
        new_key = f"{layer_name}.{block_name}.downsample.1.{split_keys[-1]}"
    elif block_id == 1 and module == "proj":
        new_key = f"{layer_name}.{block_name}.downsample.0.{split_keys[-1]}"
    elif module == "f":
        if split_keys[3] == "a_bn":
            module_name = "bn1"
        elif split_keys[3] == "b_bn":
            module_name = "bn2"
        elif split_keys[3] == "c_bn":
            module_name = "bn3"
        elif split_keys[3] == "a":
            module_name = "conv1"
        elif split_keys[3] == "b":
            module_name = "conv2"
        elif split_keys[3] == "c":
            module_name = "conv3"
        new_key = f"{layer_name}.{block_name}.{module_name}.{split_keys[-1]}"
    else:
        raise ValueError(f"Unsupported conversion of key {model_key}")
    print(f"Convert {model_key} to {new_key}")
    state_dict[new_key] = model_weight
    converted_names.add(model_key)


def convert(src, dst):
    """Convert keys in pycls pretrained RegNet models to mmdet style."""
    # load caffe model
    regnet_model = torch.load(src)
    blobs = regnet_model["model_state"]
    # convert to pytorch style
    state_dict = OrderedDict()
    converted_names = set()
    for key, weight in blobs.items():
        if "stem" in key:
            convert_stem(key, weight, state_dict, converted_names)
        elif "head" in key:
            convert_head(key, weight, state_dict, converted_names)
        elif key.startswith("s"):
            convert_reslayer(key, weight, state_dict, converted_names)

    # check if all layers are converted
    for key in blobs:
        if key not in converted_names:
            print(f"not converted: {key}")
    # save checkpoint
    checkpoint = dict()
    checkpoint["state_dict"] = state_dict
    torch.save(checkpoint, dst)


def main():
    parser = argparse.ArgumentParser(description="Convert model keys")
    parser.add_argument("src", help="src detectron model path")
    parser.add_argument("dst", help="save path")
    args = parser.parse_args()
    convert(args.src, args.dst)


if __name__ == "__main__":
    main()
